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Добірка наукової літератури з теми "ENSEMBLE LEARNING TECHNIQUE"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "ENSEMBLE LEARNING TECHNIQUE"

1

ACOSTA-MENDOZA, NIUSVEL, ALICIA MORALES-REYES, HUGO JAIR ESCALANTE, and ANDRÉS GAGO-ALONSO. "LEARNING TO ASSEMBLE CLASSIFIERS VIA GENETIC PROGRAMMING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 07 (2014): 1460005. http://dx.doi.org/10.1142/s0218001414600052.

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Анотація:

This paper introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifier's outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models' redundancy and diversity. In this research, a GP-based approach to learn fusion functions that combine classifiers outputs is proposed. Heterogeneous ensembles are aimed in this study, these models use individ

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2

Reddy, S. Pavan Kumar, and U. Sesadri. "A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 8 (2013): 1913–21. http://dx.doi.org/10.24297/ijct.v10i8.1468.

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Анотація:

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a BSA (Bootstrap Aggregation) is a machine learning ensembleÂ meta-

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3

Goyal, Jyotsana. "IMPROVING CLASSIFICATION PERFORMANCE USING ENSEMBLE LEARNING APPROACH." BSSS Journal of Computer 14, no. 1 (2023): 63–75. http://dx.doi.org/10.51767/jc1409.

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Анотація:

The data mining techniques are used for evaluation of the data in order to find and represent the data in such manner by which the applications are becomes beneficial. Therefore, different kinds of computational algorithms and modeling’s are incorporated for analyzing the data. These computational algorithms are help to understand the data patterns and their application utility. The data mining algorithms supports supervised as well as unsupervised techniques of data analysis. This work is aimed to investigate about the supervised learning technique specifically performance improvements on cla

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4

Cawood, Pieter, and Terence Van Zyl. "Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion." Forecasting 4, no. 3 (2022): 732–51. http://dx.doi.org/10.3390/forecast4030040.

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Анотація:

The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Ba

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5

Lenin, Thingbaijam, and N. Chandrasekaran. "Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms." Webology 18, Special Issue 01 (2021): 183–95. http://dx.doi.org/10.14704/web/v18si01/web18053.

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Анотація:

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random

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6

Arora, Madhur, Sanjay Agrawal, and Ravindra Patel. "Machine Learning Technique for Predicting Location." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 639–45. http://dx.doi.org/10.37391/ijeer.110254.

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Анотація:

In the current era of internet and mobile phone usage, the prediction of a person's location at a specific moment has become a subject of great interest among researchers. As a result, there has been a growing focus on developing more effective techniques to accurately identify the precise location of a user at a given instant in time. The quality of GPS data plays a crucial role in obtaining high-quality results. Numerous algorithms are available that leverage user movement patterns and historical data for this purpose. This research presents a location prediction model that incorporates data

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7

Rahimi, Nouf, Fathy Eassa, and Lamiaa Elrefaei. "An Ensemble Machine Learning Technique for Functional Requirement Classification." Symmetry 12, no. 10 (2020): 1601. http://dx.doi.org/10.3390/sym12101601.

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Анотація:

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifyin

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8

., Hartono, Opim Salim Sitompul, Erna Budhiarti Nababan, Tulus ., Dahlan Abdullah, and Ansari Saleh Ahmar. "A New Diversity Technique for Imbalance Learning Ensembles." International Journal of Engineering & Technology 7, no. 2.14 (2018): 478. http://dx.doi.org/10.14419/ijet.v7i2.11251.

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Анотація:

Data mining and machine learning techniques designed to solve classification problems require balanced class distribution. However, in reality sometimes the classification of datasets indicates the existence of a class represented by a large number of instances whereas there are classes with far fewer instances. This problem is known as the class imbalance problem. Classifier Ensembles is a method often used in overcoming class imbalance problems. Data Diversity is one of the cornerstones of ensembles. An ideal ensemble system should have accurrate individual classifiers and if there is an err

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9

Teoh, Chin-Wei, Sin-Ban Ho, Khairi Shazwan Dollmat, and Chuie-Hong Tan. "Ensemble-Learning Techniques for Predicting Student Performance on Video-Based Learning." International Journal of Information and Education Technology 12, no. 8 (2022): 741–45. http://dx.doi.org/10.18178/ijiet.2022.12.8.1679.

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Анотація:

The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era has promoted the rise of the big data era in educational data. This situation has created an opportunity for an educator to utilize the available data from MOOCs to facilitate student learning and performance. Therefore, this research study aims to introduce three types of ensemble learning methods, which are stacking, boosting, and bagging, to predict student performance. These techniques combine the advantage of feature selection method and Synthetic Minority Oversampling Techn

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10

Hussein, Salam Allawi, Alyaa Abduljawad Mahmood, and Emaan Oudah Oraby. "Network Intrusion Detection System Using Ensemble Learning Approaches." Webology 18, SI05 (2021): 962–74. http://dx.doi.org/10.14704/web/v18si05/web18274.

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Анотація:

To mitigate modern network intruders in a rapidly growing and fast pattern changing network traffic data, single classifier is not sufficient. In this study Chi-Square feature selection technique is used to select the most important features of network traffic data, then AdaBoost, Random Forest (RF), and XGBoost ensemble classifiers were used to classify data based on binary-classes and multi-classes. The aim of this study is to improve detection rate accuracy for every individual attack types and all types of attacks, which will help us to identify attacks and particular category of attacks.

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